CVE-2021-37684 in TensorFlow
Summary
by MITRE • 08/13/2021
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0. We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 08/17/2021
The vulnerability identified as CVE-2021-37684 affects TensorFlow Lite implementations where pooling operations are susceptible to division by zero errors due to insufficient validation of divisor parameters. This flaw exists within the core machine learning framework's mobile and embedded inference capabilities, specifically impacting the mathematical computations used in pooling layers that are fundamental to convolutional neural networks. The issue represents a critical security concern as it can lead to application crashes and potential denial of service conditions during model execution, particularly in resource-constrained environments where TensorFlow Lite is commonly deployed.
The technical root cause stems from the absence of proper input validation within the pooling implementation functions, where the code fails to verify that divisor values are non-zero before performing division operations. This type of vulnerability aligns with CWE-369, which specifically addresses division by zero errors in software implementations. The flaw manifests when TensorFlow Lite processes neural network models containing pooling layers that utilize zero-valued divisors, causing the execution to terminate abruptly or behave unpredictably. The vulnerability affects multiple versions of TensorFlow 2.x series, with affected releases including 2.3.4, 2.4.3, 2.5.1, and earlier versions that remain within the supported maintenance window, demonstrating the widespread impact across the framework's release lifecycle.
The operational impact of this vulnerability extends beyond simple application crashes, potentially compromising the reliability and stability of machine learning deployments across various platforms including mobile devices, embedded systems, and edge computing environments. Attackers could exploit this weakness to cause service disruption in applications that rely on TensorFlow Lite for inference tasks, particularly in production systems where model execution must remain robust and uninterrupted. The vulnerability affects the broader machine learning ecosystem as it impacts the foundational inference capabilities that power numerous applications from image recognition to natural language processing. The fix implemented in the GitHub commit addresses the core validation issue by introducing proper divisor checks before division operations are performed, ensuring that pooling computations handle edge cases appropriately. This remediation follows established security practices for preventing arithmetic exceptions and aligns with ATT&CK technique T1499.004, which covers system network configuration modification, as the vulnerability could potentially be leveraged to disrupt system availability through controlled input manipulation.
Organizations using TensorFlow Lite implementations should immediately upgrade to TensorFlow 2.6.0 or apply the cherry-picked fixes to their supported versions to mitigate this vulnerability. The patch implementation demonstrates proper defensive programming practices by incorporating input validation checks that prevent the execution path from reaching invalid mathematical operations. Security teams should monitor their deployments for any instances where TensorFlow Lite models might be processing user-supplied inputs that could potentially lead to zero-valued divisor scenarios, particularly in applications involving dynamic model loading or parameter manipulation. The vulnerability serves as a reminder of the importance of input validation in mathematical operations within machine learning frameworks, where seemingly benign operations can become security risks when proper error handling is absent.